Biomass removal promotes plant diversity after short-term de-intensification of managed grasslands

Land-use intensification is one of the main drivers threatening biodiversity in managed grasslands. Despite multiple studies investigating the effect of different land-use components in driving changes in plant biodiversity, their effects are usually studied in isolation. Here, we establish a full factorial design crossing fertilization with a combined treatment of biomass removal, on 16 managed grasslands spanning a gradient in land-use intensity, across three regions in Germany. Specifically, we investigate the interactive effects of different land-use components on plant composition and diversity using structural equation modelling. We hypothesize that fertilization and biomass removal alter plant biodiversity, directly and indirectly, mediated through changes in light availability. We found that, direct and indirect effects of biomass removal on plant biodiversity were larger than effects of fertilization, yet significantly differed between season. Furthermore, we found that indirect effects of biomass removal on plant biodiversity were mediated through changes in light availability, but also by changes in soil moisture. Our analysis thus supports previous findings, that soil moisture may operate as an alternative indirect mechanism by which biomass removal may affect plant biodiversity. Most importantly, our findings highlight that in the short-term biomass removal can partly compensate the negative effects of fertilization on plant biodiversity in managed grasslands. By studying the interactive nature of different land-use drivers we advance our understanding of the complex mechanisms controlling plant biodiversity in managed grasslands, which ultimately may help to maintain higher levels of biodiversity in grassland ecosystems.

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Land-use intensification is one of the major drivers threatening biodiversity in managed grasslands. In particular, different land-use components may influence plant biodiversity both directly but also indirectly, while possibly interacting at the same time. However, the effects of different land-use components on plant biodiversity are usually studied in isolation, limiting our understanding of their interactive nature in mediating plant biodiversity.
With this study, we combine observations and experimental treatments as part of a full factorial design across 16 managed grassland fields in Germany. Specifically, using structural equation modelling, we test how different land-use components (mowing, grazing and fertilization) affect plant biodiversity, both directly and indirectly through changed in light availability and/or soil moisture. We found that surprisingly, direct and indirect effects of biomass removal on plant biodiversity exceeded the effects of fertilization. Further, we found that indirect effects of biomass removal on plant biodiversity were mediated not only through changes in light availability, but also through changes in soil moisture.
An important implication of our study is that biomass removal can partly compensate the negative effects of fertilization on plant biodiversity in managed grasslands. We support findings from previous studies, that soil moisture may operate as an alternative indirect mechanism by which biomass removal may affect plant biodiversity. Studying the interactive nature of different land-use drivers will advance our understanding of the complex mechanisms controlling plant biodiversity in managed grasslands. Ultimately, this knowledge may help us to maintain higher levels of biodiversity in grassland ecosystems.

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Land-use intensification is one of the main drivers threatening biodiversity in managed 26 grasslands. Despite multiple studies investigating the effect of different land-use components in 27 driving changes in plant biodiversity, their effects are usually studied in isolation. Here, we establish a 28 full factorial design crossing fertilization with a combined treatment of biomass removal, on 16 29 managed grasslands spanning a gradient in land-use intensity, across three regions in Germany. 30 Specifically, we investigate the interactive effects of different land-use components on plant 31 composition and diversity using structural equation modelling. We hypothesize that fertilization and 32 biomass removal alter plant biodiversity, directly and indirectly, mediated through changes in light 33 availability. We found that, direct and indirect effects of biomass removal on plant biodiversity were 34 larger than effects of fertilization, yet significantly differed between season. Furthermore, we found 35 that indirect effects of biomass removal on plant biodiversity were mediated through changes in light 36 availability, but also by changes in soil moisture. Our analysis thus supports previous findings, that soil 37 moisture may operate as an alternative indirect mechanism by which biomass removal may affect 38 plant biodiversity. Most importantly, our findings highlight that biomass removal can partly 39 richness but also Shannon diversity [ Specifically, we aim to distinguish between direct and indirect effects of fertilization on plant 118 biodiversity, and how biomass removal due to mowing/grazing mediates these pathways. We thus aim 119 to understand how different drivers of land-use extensification may, or may not, promote plant 120 biodiversity. To address these questions, we designed a full factorial experiment, replicated within Schorfheide-Chorin). Due to various climatic and edaphic differences, these regions vary in several 139 environmental conditions, such as temperature and soil fertility (deep layers of organic soils) being 140 highest in north-west, or annual mean precipitation and elevation being highest in the south-west (see 141 [46] for more details on regional differences). All 16 grasslands were commercially managed, spanning 142 a gradient of background land-use intensity (LUI) which is a composite measure of mowing frequency, 143 livestock units and amount of fertilization (see 17 Table). By including multiple regions and spanning a gradient in background grassland land-use intensity (rather than conducting a more conventional 145 experiment in highly controlled settings), we aimed to investigate how the extensification of different 146 grassland land-use drivers (fertilization vs mowing/grazing) interactively drive plant biodiversity in 147 semi-natural systems. Across all studied grasslands, the most 10 abundant species were: Poa pratensis 148 L., Lolium perenne L., Dactylis glomerata L., Taraxacum  representing grasslands managed at relatively high intensities (S17 Table) three replicates per subplot. In order to account for biomass that has been removed due to mowing 197 or grazing when calculating biomass production for the summer season, we further used data on 198 grazing duration, livestock units per area and hay yield of mowing derived from yearly standardized 199 questionnaires of land-owners and farmers (for more information see 50), which we obtained from 200 the database BExIS (see BExIS dataset ID 26487, http://doi.org/10.17616/R32P9Q). In spring, biomass 201 production was considered equal to standing biomass in spring, as most fields were hardly grazed or 202 not mown before our biomass measurements took place (except for one field). Additionally, we 203 quantified background fertilization intensity (kg N ha -1 year -1 , also derived from yearly std. variable and rising-plate meter measurements (in 0.5 cm increments) as predictor variable. Prior to calculating the calibration model, we excluded data from five subplots due to measurement errors. 220 Following the calibration model, biomass estimates explained 83.22 % of the variance in actual 221 standing biomass (t = 37.94, p > 0.01, RSD = 38.85 g m -2 , S18 Table). Using the intercept and slope of 222 the calibration model, we converted the biomass estimates into actual standing biomass following the 223 equation: dry biomass (g m -2 ) = -38.66 + 8.68 × Plate meter measurement. In addition to standing 224 biomass, we also quantified biomass production in all subplots which was defined as all biomass 225 produced in a given time (Spring until Summer) including biomass removed by mowing or grazing. Only 226 biomass removed by higher trophic levels other than livestock (e.g. herbivorous arthropods) could not 227 be included in our biomass production estimate. We quantified biomass production following Riehl 228 as the sum for all mowing events between the end of the first field survey and the end of the second 234 field survey (14 th of August). These periods were also applied to calculate the grazing duration and 235 livestock units per subplot. We only calculated biomass production in summer as in this season we 236 expect a mismatch between standing biomass and biomass production due to land-use (e.g. removal 237 of biomass due to mowing or grazing), while in spring biomass measurements were in most plots done 238 before mowing and grazing events took place, and hence standing biomass was almost equivalent to 239 biomass production of the early spring season. To quantify light competition in each subplot we 240 calculated the inverse ratio between the average light availability of photosynthetically active radiation above canopy height and ground level (light availability hereafter) per subplot, with light availability 242 close to 0 indicating low light availability and close to 1 indicating higher light availability. Additionally, 243 we obtain a mean background fertilization intensity value over the time before our experiment was 244 set up (2017-2019), background fertilization intensity was averaged across time.

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We expected that fertilization can both directly, but also indirectly (mediated via changes in 276 biomass production, standing biomass and light availability) affect species richness and Shannon 277 diversity. Furthermore, we expected biomass removal to mediate the indirect effect of fertilization on 278 species richness/Shannon diversity by removing biomass and thereby decrease light competition. We 279 additionally tested for an indirect relationship between fertilization and biomass removal via soil 280 moisture. To guarantee comparability between spring and summer data, we used soil moisture as an 281 additional indirect pathway in both spring and summer based models. To test these assumptions, we 282 constructed a hypothesis driven causal model using linear mixed-effect models within a PiecewiseSEM 283 [65]. In our models, we converted the treatment ID into the dummy variables "fertilization", and 284 "biomass removal", with "0" indicating absence and "1" presence of the respective land-use 285 component. We further log-transformed light availability, to meet model assumptions regarding 286 linearity. To statistically correct for the influence of year and sampling date we further constructed a 287 linear model with each continuous variable (species richness, species diversity, biomass production, 288 standing biomass and log light availability) as response, with year and sampling date as fixed effect and 289 with an interaction term. Finally, we extracted the model residuals (without the explained variance by 290 year and sampling date) which were used as input for all further SEM models.
We constructed four separate models, testing the causal relationship between the land-use 292 drivers fertilization and biomass removal with species richness or Shannon diversity in spring and 293 summer separately. First, we ran the initial SEM models as a list of causal relationships. Secondly, we 294 inspected all initial model results for goodness-of-fit tests and Fisher's C statistics, and, if necessary, 295 added predictors that significantly improved the model fit with p-values higher than 0.05. During this 296 process, in all summer models (not in spring since biomass production was not included in spring 297 models), we included a correlated error structure between biomass production and soil moisture. This 298 was done in order to increase the goodness-of-fit of the models, while we deliberately did not include 299 any causal relationship between biomass production and soil moisture since it was difficult to 300 distinguish between cause and effect. To statistically correct for the confounding effects of covarying 301 factors we included background fertilization intensity of the time before the experiment was set up 302 (mean of 2017-2019) as covariate for models predicting species richness/diversity, standing biomass 303 and biomass production. In all models, field was treated as a random factor nested within study region 304 by using the lme function from the package nlme  Table), we generally observed a lower species richness in fertilized & reduced biomass removal subplots compared to all other 315 treatments (although not statistically significant). However, in the Hainich-Dün in spring 2021, species 316 richness was found to be marginally significantly, lower in subplots which were fertilized but had 317 reduced biomass removal (fertilized & reduced biomass removal) compared to subplots with no 318 fertilization but biomass removal (unfertilized & biomass removal) (S2 Table). Our NMDS results 319 showed no significant differences in community composition between the different treatments in 320 neither of the regions, nor in any of the different seasons of the year 2021 (3 Fig, S7 Table).  Table).  Table).  Table) and Shannon diversity (Fisher's C = 18.728, df = 14, p = 0.176, n = 105, S3 343 Table) well (3A and B Fig). For the SEM model on the spring data, we found no evidence for direct or 344 indirect (via light availability or soil moisture) causal pathways between fertilization or biomass 345 removal and species richness/Shannon diversity. We found a strong partial effect between standing 346 biomass (hereafter simply 'biomass') and light availability in the understory of the vegetation (direct 347 path coeff. = −0.75). However, we did not find any significant relationship between light availability 348 and species richness/Shannon diversity. In contrast, soil moisture was found to be directly positively 349 related to species richness/Shannon diversity (direct path coeff. = 0.37, direct path coeff. = 0.34, 350 respectively). Additionally, we found that biomass removal strongly reduced the accumulation of 351 biomass (direct path coeff. = −0.46), although no significant direct or indirect pathways between 352 biomass removal and species richness or Shannon diversity were found. Overall, the SEM models in 353 spring explained only 15 % of the variance observed in species richness and 14 % of the variance in 354 species diversity. The most important predictor for species richness in spring was soil moisture (partial 355 r 2 = 0.16), followed by fertilization (partial r 2 = 0.005), light availability (partial r 2 = 0.005) and biomass 356 removal (partial r 2 = 0.004). For Shannon diversity, the most important predictor was soil moisture 357 (partial r 2 = 0.128), followed by biomass removal (partial r 2 = 0.013), light availability (partial r 2 = 0.012), 358 fertilization (partial r 2 = 0.004) and background fertilization intensity (partial r 2 = 0.003). An NMDS 359 analysis of the spring data showed that the effects of soil moisture on species richness and Shannon 360 diversity cannot be explained by changes in species composition (5 Fig, S9 Table). 361 In summer, the SEM fitted species richness (Fisher's C = 24.063, df = 22, p = 0.344, n = 117, S5 362 Table) and Shannon diversity (Fisher's C = 23.915, df = 22, p = 0.352, n = 117, S6 Table) Table). We found no evidence for an indirect 366 causal pathway between biomass removal and species richness/Shannon diversity, mediated via 367 changes in light availability. However, we did find evidence for an indirect causal pathway between 368 biomass removal and species richness via soil moisture (4 Fig, S5 Table). For both models considering 369 species richness or Shannon diversity, biomass removal led to an increase in biomass production 370 (direct path coeff. = 0.57), while at the same time reducing standing biomass (direct path coeff. = -371 0.74). Furthermore, the accumulation of standing biomass led to a strong decrease in light availability 372 (direct path coeff. = −0.82) and soil moisture (direct path coeff. = −0.24). We also found strong partial 373 effects of biomass production on standing biomass (direct path coeff. = 0.23). However, we did not 374 detect any strong direct or indirect effects of fertilization or background fertilization intensity on 375 neither species richness, Shannon diversity (1 Table) nor biomass production. Overall, the SEM models 376 in summer explained 11 % of the variance observed in species richness and 10 % of the variance in 377 Shannon diversity. The most important predictor for species richness in summer was soil moisture 378 (partial r 2 = 0.104), followed by biomass removal (partial r 2 = 0.043), fertilization (partial r 2 = 0.025), 379 light availability (partial r 2 = 0.017) and background fertilization intensity (partial r 2 = 0.008). For 380 Shannon diversity, the most important predictor was biomass removal (partial r 2 = 0.048), followed by 381 soil moisture (partial r 2 = 0.044), background fertilization intensity (partial r 2 = 0.037), light availability 382 (partial r 2 = 0.034) and fertilization (partial r 2 = 0.021). The NMDS analysis of the summer data showed 383 that the positive effect of soil moisture on species richness in the summer season can be partly 384 explained by changes in species composition (5 Fig, S9 Table), with moist plots being positively 385 associated with both species richness, diversity and the first two NMDS axes, which represented a shift 386 from communities showing higher abundances of Rumex acetosella in dry plots towards a higher 387 abundance of Potentilla erecta in plots with a relatively high soil moisture.  Table).   Shannon diversity. We hypothesized that fertilization controls species richness and Shannon diversity 430 both directly (e.g. by eutrophication, see [67]) and indirectly, via increased light competition resulting 431 from increased standing biomass, [11,15] or alternatively, through changes in soil moisture [17]. 432 However, we did not detect strong direct or indirect effects of fertilization on species richness or 433 Shannon diversity in spring and summer, while (non-significant) effects were mainly negative (albeit 434 weakly), as expected (1 Table). One possible explanation for the lack of strong direct or indirect effects 435 of fertilization on species richness or Shannon diversity might be that species gains in response to 436 cessation of fertilization need more time to emerge, so that only small differences between fertilized 437 and unfertilized treatments are visible in the short term. In general, light limitation as a consequence of fertilization-induced increases in standing biomass is suggested to negatively affect plant species 439 richness and Shannon diversity, by changing competitive abilities of plants, consequentially favouring 440 fast-growing and tall species [15,20,21]. However, in the present study we did not observe strong 441 changes in the plant community composition (2 Fig). Only the plant community composition in 442 Schorfheide-Chorin showed a tendency to differ between the land-use treatments (3 Fig, S7 Table).  Table). Furthermore, we observed that biomass removal increased biomass production, which has 454 also been shown in previous studies [70], likely due to compensatory regrowth. However, direct effects 455 of biomass removal on species richness or diversity differed strongly between seasons. In spring, 456 biomass removal did not significantly affect species richness and Shannon diversity (although non-457 significant, weak negative effects were observed), while direct effects in summer were found to be 458 significantly positive (4 Fig, 1 Table). Previous studies have found that early-season biomass removal 459 events reduce species richness [9], likely explaining the negative (although weak and non-significant) 460 relationships between biomass removal and both richness and Shannon diversity observed in spring. biomass removal due to an extinction debt (due to delayed extinction). Similarly, we could expect 486 increased plant biodiversity only to be visible at the longer term, in plots with reduced fertilization 487 intensity, due to an immigration credit. In contrast to the observed relationships in spring, we found a 488 significant negative indirect effect of biomass removal on Shannon diversity in summer mediated 489 through changes in light availability, while species richness was significantly positively affected through 490 biomass removal-induced changes in soil moisture (4 Fig, 1  our results suggest that biomass removal may indirectly promote plant species richness due to 504 increased soil moisture in managed grasslands. Further, we found that the positive effect of biomass 505 removal mediated through soil moisture on species richness in summer, could also be partly explained 506 by shifts in the community composition (5 Fig). For example Potentilla erecta (L.) Raeusch., was slightly 507 associated with moist plots, while dryer plots were slightly associated with Rumex acetosella L.. 508 However, as our experiment only aimed to manipulate the intensity of land-use drivers, we cannot 509 fully unravel the relative importance of land-use mediated changes in light availability in comparison 510 to soil moisture and how that affects plant biodiversity. In the present study, the effects of both 511 mowing and grazing were summarized mainly as effects of biomass removal. However, although both 512 mowing and grazing have been found to promote plant biodiversity in grasslands [27, 28, 32, 33], the 513 underlying mechanisms may differ, with grazing causing more patchy biomass removal, while mowing 514 having more uniform effects [78]. As most of the grassland sites studied here were managed as 515 meadows, it is possible that direct and indirect effects of biomass removal on plant biodiversity 516 observed in this study were mostly driven by effects of mowing. Thus, to truly understand the complex 517 interaction between different land-use drivers on plant biodiversity in managed grasslands it is necessary to not only disentangle the effects of fertilization and biomass removal, but also account for 519 the type of biomass removal. 520 To our knowledge, this study presents the first attempt to not only statistically [9], but also 521 experimentally separate the effects of different drivers of land-use extensification, namely a cessation 522 in fertilization, and a reduction in biomass removal, on plant biodiversity in managed grasslands. In 523 particular, and contrary to previous studies [15], we used high land-use intensity as a default in 524 comparison to treatments in which the intensity of certain land-use drivers was reduced. This enabled 525 us to disentangle the direct and indirect effects of reduced fertilization and biomass removal in 526 managed grasslands. We found that indirect effects of land-use on plant biodiversity were mainly 527 driven by biomass removal, in particular in summer, mediated through both light availability and soil 528 moisture. Specifically, the importance of soil moisture for plant biodiversity found in this study 529 supports findings from previous studies [17,40], suggesting that indirect effects of land-use mediated 530 through light availability may depend on other environmental factors (such as soil moisture) that limit 531 plant growth. Importantly, as climate change scenarios predict increasing temperatures in the future 532 [79], the relative importance of indirect pathways of land-use mediated through changes in soil 533 moisture might even increase. Overall, we did not find that indirect effects through changes in standing 534 biomass were of greater importance than direct effects of land-use, in particular biomass removal. In 535 the present short-term study, we could not prove that a cessation in fertilization improves plant 536 biodiversity, however, we did show that in these studied managed grasslands with likely high soil 537 nutrient loads, decreasing biomass removal through a reduction in grazing intensity or mowing 538 frequency negatively affects plant diversity. Still, further research is needed to disentangle the complex 539 interaction of different land-use drivers in managed grasslands. Specifically, as our experiment focused 540 on grasslands sites managed at moderate to high land-use intensities, future studies should investigate 541 the interactive effects of different land-use drivers on plant biodiversity along the full gradient of land-542 use, while further comparing direct and indirect short vs long term land-use mediated effects.
Nevertheless, this study may help us to understand how different land-use drivers interactively affect 544 and thereby control biodiversity, which is crucial for informing biodiversity conservation. 545 6 Acknowledgements 546 We thank Svenja Kunze, Ralph Bolliger, Uta Schumacher, Jörg Hailer, Christin Schreiber, Victoria 547 Henning and many students for helping us to collect data in the field. We also thank Christian Wirth 548 and Christiane Roscher for their input during the conceptual phase of this paper. Also many thanks to